{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Define Something in Advance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def U(x,y):\n",
    "    return np.cos(y-x)\n",
    "\n",
    "def V(x,y):\n",
    "    return np.sin(y-x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Initialize the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta = 0.1*np.pi\n",
    "x_domain = np.arange(0, np.pi, delta)\n",
    "y_domain = np.arange(0, np.pi, delta)\n",
    "x_mesh,y_mesh = np.meshgrid(x_domain, y_domain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = x[0];y = x[1];\n",
    "    uxy = x[2];vxy = x[3]; \n",
    "    dudx = x[4];dvdx = x[5];\n",
    "    dudy = x[6];dvdy = x[7];\n",
    "    du2dx2 = x[8];dv2dx2 = x[9];\n",
    "    du2dy2 = x[10];dv2dy2 = x[11];\n",
    "    du2dxdy = x[12];dv2dxdy = x[13];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: (x[4]+x[11]+x[3]-(x[2]**2+x[3]**2)*x[3])**2))\n",
    "governing_equation_components.append(Lambda(lambda x: (x[5]-x[10]-x[2]+(x[2]**2+x[3]**2)*x[2])**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_mesh*0+1\n",
    "#governing_equation_mask[0,:]=0\n",
    "#governing_equation_mask[-1,:]=0\n",
    "#governing_equation_mask[:,0]=0\n",
    "#governing_equation_mask[:,-1]=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx = governing_equation_mask*(x_mesh*0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: \n",
    "    x = x[0];y = x[1];\n",
    "    uxy = x[2];vxy = x[3]; \n",
    "    dudx = x[4];dvdx = x[5];\n",
    "    dudy = x[6];dvdy = x[7];\n",
    "    du2dx2 = x[8];dv2dx2 = x[9];\n",
    "    du2dy2 = x[10];dv2dy2 = x[11];\n",
    "    du2dxdy = x[12];dv2dxdy = x[13];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: (x[2]-tf.cos(x[1]-x[0]))**2))\n",
    "observation_components.append(Lambda(lambda x: (x[3]-tf.sin(x[1]-x[0]))**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    format: [x,y,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "# bottom\n",
    "for x in x_domain:\n",
    "    y = y_domain[0]\n",
    "    comb = [1,1]\n",
    "    v = 0\n",
    "    observation_data.append([x,y,comb,v])\n",
    "# left\n",
    "for y in y_domain:\n",
    "    x = x_domain[0]\n",
    "    comb = [1,1]\n",
    "    v = 0\n",
    "    observation_data.append([x,y,comb,v])  \n",
    "# up\n",
    "for x in x_domain:\n",
    "    y = y_domain[-1]\n",
    "    comb = [1,1]\n",
    "    v = 0\n",
    "    observation_data.append([x,y,comb,v])   \n",
    "# right\n",
    "for y in y_domain:\n",
    "    x = x_domain[-1]\n",
    "    comb = [1,1]\n",
    "    v = 0\n",
    "    observation_data.append([x,y,comb,v])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]], [[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_x2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-2.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[-1.0/delta]]], [[[1.0/delta]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_y2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]]], [[[-2.0/delta**2]]], [[[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_xy_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[1.0/delta**2]], [[-1.0/delta**2]]], [[[-1.0/delta**2]], [[1.0/delta**2]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def laplace_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[[0]], [[1.0/delta**2]], [[0]]], [[[1.0/delta**2]], [[-4.0/delta**2]], [[1.0/delta**2]]], [[[0]], [[1.0/delta**2]], [[0]]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "gradient_x = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[1,2], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_x_kernal_init)\n",
    "gradient_x2 = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[1,3], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_x2_kernal_init)\n",
    "gradient_y = keras.layers.Conv2D(filters=1, \n",
    "                                     kernel_size=[2,1], \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_y_kernal_init)\n",
    "gradient_y2 = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[3,1], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_y2_kernal_init)\n",
    "gradient_xy = keras.layers.Conv2D(filters=1, \n",
    "                                       kernel_size=[2,2], \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_xy_kernal_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1,1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1)),\n",
    "          keras.layers.Input(shape=(len(observation_data),len(observation_components)*4)),\n",
    "          keras.layers.Input(shape=(len(y_domain),len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv2DTranspose(filters=2, \n",
    "                                            kernel_size=[len(y_domain)+4,len(x_domain)+4], \n",
    "                                            activation='linear')(inputs[0])\n",
    "field = keras.layers.Conv2D(filters=2, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation='linear')(hidden_field)\n",
    "                            #activation=Lambda(lambda x: x+tf.tanh(x)))(hidden_field)\n",
    "\n",
    "coordinates_x = inputs[1]\n",
    "coordinates_y = inputs[2]\n",
    "\n",
    "uxy_field = field[:,:,:,0:1]\n",
    "vxy_field = field[:,:,:,1:2]\n",
    "\n",
    "dudx_field = gradient_x(uxy_field[:,:,:,0:1])\n",
    "dvdx_field = gradient_x(vxy_field[:,:,:,0:1])\n",
    "\n",
    "dudy_field = gradient_y(uxy_field[:,:,:,0:1])\n",
    "dvdy_field = gradient_y(vxy_field[:,:,:,0:1])\n",
    "\n",
    "d2udx2_field = gradient_x2(uxy_field[:,:,:,0:1])\n",
    "d2vdx2_field = gradient_x2(vxy_field[:,:,:,0:1])\n",
    "\n",
    "d2udy2_field = gradient_y2(uxy_field[:,:,:,0:1])\n",
    "d2vdy2_field = gradient_y2(vxy_field[:,:,:,0:1])\n",
    "\n",
    "d2udxy_field = gradient_xy(uxy_field[:,:,:,0:1])\n",
    "d2vdxy_field = gradient_xy(vxy_field[:,:,:,0:1])\n",
    "\n",
    "phycial_fields = [coordinates_x,\n",
    "                  coordinates_y,\n",
    "                  uxy_field[:,1:-1,1:-1,:],\n",
    "                  vxy_field[:,1:-1,1:-1,:],\n",
    "                  dudx_field[:,1:-1,1:,:],\n",
    "                  dvdx_field[:,1:-1,1:,:],\n",
    "                  dudy_field[:,1:,1:-1,:],\n",
    "                  dvdy_field[:,1:,1:-1,:],\n",
    "                  d2udx2_field[:,1:-1,:,:],\n",
    "                  d2vdx2_field[:,1:-1,:,:],\n",
    "                  d2udy2_field[:,:,1:-1,:],\n",
    "                  d2vdy2_field[:,:,1:-1,:],\n",
    "                  d2udxy_field[:,1:,1:,:],\n",
    "                  d2vdxy_field[:,1:,1:,:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv2D(filters=1, \n",
    "                                             kernel_size=[1,1], \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)*inputs[4]\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)*inputs[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "concat_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_x_position_index_list = []\n",
    "right_x_position_index_list = []\n",
    "left_x_position_weight_list = []\n",
    "right_x_position_weight_list = []\n",
    "\n",
    "bottom_y_position_index_list = []\n",
    "top_y_position_index_list = []\n",
    "bottom_y_position_weight_list = []\n",
    "top_y_position_weight_list = []\n",
    "\n",
    "\n",
    "for data in observation_data:\n",
    "    \n",
    "    left_x_position_index = int(np.floor((data[0] - x_domain[0])/delta))\n",
    "    right_x_position_index = left_x_position_index + 1\n",
    "    left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "    right_x_position_weight = 1-left_x_position_weight\n",
    "    \n",
    "    bottom_y_position_index = int(np.floor((data[1] - y_domain[0])/delta))\n",
    "    top_y_position_index = bottom_y_position_index + 1\n",
    "    bottom_y_position_weight = 1-(data[1] - (y_domain[0]+delta*bottom_y_position_index))/delta\n",
    "    top_y_position_weight = 1-bottom_y_position_weight\n",
    "    \n",
    "    if data[0] <= x_domain[0] + 1e-8:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    if data[0] >= x_domain[-1] - 1e-8:\n",
    "        left_x_position_index = len(x_domain)-2\n",
    "        right_x_position_index = len(x_domain)-1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    if data[1] <= y_domain[0] + 1e-8:\n",
    "        bottom_y_position_index = 0\n",
    "        top_y_position_index = 1\n",
    "        bottom_y_position_weight = 1\n",
    "        top_y_position_weight = 0\n",
    "    if data[1] >= y_domain[-1] - 1e-8:\n",
    "        bottom_y_position_index = len(y_domain)-2\n",
    "        top_y_position_index = len(y_domain)-1\n",
    "        bottom_y_position_weight = 0\n",
    "        top_y_position_weight = 1\n",
    "    \n",
    "    left_x_position_index_list.append(left_x_position_index)\n",
    "    right_x_position_index_list.append(right_x_position_index)\n",
    "    left_x_position_weight_list.append(left_x_position_weight)\n",
    "    right_x_position_weight_list.append(right_x_position_weight)\n",
    "\n",
    "    bottom_y_position_index_list.append(bottom_y_position_index)\n",
    "    top_y_position_index_list.append(top_y_position_index)\n",
    "    bottom_y_position_weight_list.append(bottom_y_position_weight)\n",
    "    top_y_position_weight_list.append(top_y_position_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_bottom_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,bottom_y_position_index_list)])\n",
    "left_top_indices = tf.constant([[0,y,x] for x,y in zip(left_x_position_index_list,top_y_position_index_list)])\n",
    "right_bottom_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,bottom_y_position_index_list)])\n",
    "right_top_indices = tf.constant([[0,y,x] for x,y in zip(right_x_position_index_list,top_y_position_index_list)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_left_bottom_part = Lambda(lambda x: tf.gather_nd(x,left_bottom_indices))(concat_observation_components)\n",
    "observation_data_left_top_part = Lambda(lambda x: tf.gather_nd(x,left_top_indices))(concat_observation_components)\n",
    "observation_data_right_bottom_part = Lambda(lambda x: tf.gather_nd(x,right_bottom_indices))(concat_observation_components)\n",
    "observation_data_right_top_part = Lambda(lambda x: tf.gather_nd(x,right_top_indices))(concat_observation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_four_part = Lambda(lambda x: tf.expand_dims(tf.concat(x,axis=-1),axis=0))([observation_data_left_bottom_part,observation_data_left_top_part,observation_data_right_bottom_part,observation_data_right_top_part])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data_interpolation_weights = np.expand_dims(\n",
    "np.asarray([np.concatenate([(np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*left_x_position_weight_list[i]*top_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*bottom_y_position_weight_list[i],\n",
    " (np.asarray([observation_data[i][2][j] for j in range(len(observation_components))]))*right_x_position_weight_list[i]*top_y_position_weight_list[i]],axis=-1) for i in range(len(observation_data))]),\n",
    "    axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "observations = Lambda(lambda x: tf.reduce_sum(x[0]*x[1],axis=-1))([inputs[3][0,:,:],observation_data_four_part])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs[:3], outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 1, 1, 1)]            0         []                            \n",
      "                                                                                                  \n",
      " conv2d_transpose (Conv2DTr  (None, 14, 14, 2)            394       ['input_1[0][0]']             \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)           (None, 12, 12, 2)            38        ['conv2d_transpose[0][0]']    \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (  (None, 12, 12, 1)            0         ['conv2d_5[0][0]']            \n",
      " SlicingOpLambda)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 12, 12, 1)            0         ['conv2d_5[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 12, 12, 1)            0         ['tf.__operators__.getitem[0][\n",
      "  (SlicingOpLambda)                                                 0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_3  (None, 12, 12, 1)            0         ['tf.__operators__.getitem_1[0\n",
      "  (SlicingOpLambda)                                                 ][0]']                        \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_4  (None, 12, 12, 1)            0         ['tf.__operators__.getitem[0][\n",
      "  (SlicingOpLambda)                                                 0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_5  (None, 12, 12, 1)            0         ['tf.__operators__.getitem_1[0\n",
      "  (SlicingOpLambda)                                                 ][0]']                        \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_6  (None, 12, 12, 1)            0         ['tf.__operators__.getitem[0][\n",
      "  (SlicingOpLambda)                                                 0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_7  (None, 12, 12, 1)            0         ['tf.__operators__.getitem_1[0\n",
      "  (SlicingOpLambda)                                                 ][0]']                        \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_8  (None, 12, 12, 1)            0         ['tf.__operators__.getitem[0][\n",
      "  (SlicingOpLambda)                                                 0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_9  (None, 12, 12, 1)            0         ['tf.__operators__.getitem_1[0\n",
      "  (SlicingOpLambda)                                                 ][0]']                        \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 12, 12, 1)            0         ['tf.__operators__.getitem[0][\n",
      " 0 (SlicingOpLambda)                                                0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 12, 12, 1)            0         ['tf.__operators__.getitem_1[0\n",
      " 1 (SlicingOpLambda)                                                ][0]']                        \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)             (None, 12, 11, 1)            2         ['tf.__operators__.getitem_2[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'tf.__operators__.getitem_3[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)           (None, 11, 12, 1)            2         ['tf.__operators__.getitem_4[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'tf.__operators__.getitem_5[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)           (None, 12, 10, 1)            3         ['tf.__operators__.getitem_6[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'tf.__operators__.getitem_7[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)           (None, 10, 12, 1)            3         ['tf.__operators__.getitem_8[0\n",
      "                                                                    ][0]',                        \n",
      "                                                                     'tf.__operators__.getitem_9[0\n",
      "                                                                    ][0]']                        \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)           (None, 11, 11, 1)            4         ['tf.__operators__.getitem_10[\n",
      "                                                                    0][0]',                       \n",
      "                                                                     'tf.__operators__.getitem_11[\n",
      "                                                                    0][0]']                       \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 10, 10, 1)]          0         []                            \n",
      "                                                                                                  \n",
      " input_3 (InputLayer)        [(None, 10, 10, 1)]          0         []                            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['tf.__operators__.getitem[0][\n",
      " 2 (SlicingOpLambda)                                                0]']                          \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['tf.__operators__.getitem_1[0\n",
      " 3 (SlicingOpLambda)                                                ][0]']                        \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d[0][0]']              \n",
      " 4 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d[1][0]']              \n",
      " 5 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d_2[0][0]']            \n",
      " 6 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d_2[1][0]']            \n",
      " 7 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d_1[0][0]']            \n",
      " 8 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 10, 10, 1)            0         ['conv2d_1[1][0]']            \n",
      " 9 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 10, 10, 1)            0         ['conv2d_3[0][0]']            \n",
      " 0 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 10, 10, 1)            0         ['conv2d_3[1][0]']            \n",
      " 1 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 10, 10, 1)            0         ['conv2d_4[0][0]']            \n",
      " 2 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_2  (None, 10, 10, 1)            0         ['conv2d_4[1][0]']            \n",
      " 3 (SlicingOpLambda)                                                                              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 446 (1.74 KB)\n",
      "Trainable params: 432 (1.69 KB)\n",
      "Non-trainable params: 14 (56.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_print_functoin(s):\n",
    "    with open('model_summary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "pde_model.summary(print_fn=model_print_functoin)\n",
    "pde_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[[1.0]]]],dtype=np.float32)\n",
    "training_input_data_0 = unit_constant\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 10, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 10, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_2 = np.expand_dims(y_mesh.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 40, 8)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_3 = observation_data_interpolation_weights.astype(np.float32)\n",
    "print(training_input_data_3.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 10, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_4 = np.expand_dims(governing_equation_mask.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 10, 10, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx.astype(np.float32),axis=[0,-1])\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 40)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[3] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1,training_input_data_2,training_input_data_3,training_input_data_4]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'tf.math.multiply_2_loss', 'lambda_12_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_, _, uxy, vxy, dudx, dvdx, dudy, dvdy, d2udx2, d2vdx2, d2udy2, d2vdy2, d2udxdy, d2vdxdy] = pde_model.predict(training_input_data[:3])\n",
    "    uxy_list.append(uxy[:,:,:,0])\n",
    "    vxy_list.append(vxy[:,:,:,0])\n",
    "    dudx_list.append(dudx[:,:,:,0])\n",
    "    dvdx_list.append(dvdx[:,:,:,0])\n",
    "    dudy_list.append(dudy[:,:,:,0])\n",
    "    dvdy_list.append(dvdy[:,:,:,0])\n",
    "    d2udx2_list.append(d2udx2[:,:,:,0])\n",
    "    d2vdx2_list.append(d2vdx2[:,:,:,0])\n",
    "    d2udy2_list.append(d2udy2[:,:,:,0])\n",
    "    d2vdy2_list.append(d2vdy2[:,:,:,0])\n",
    "    d2udxdy_list.append(d2udxdy[:,:,:,0])\n",
    "    d2vdxdy_list.append(d2vdxdy[:,:,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.6f} | total_loss: {:10.6f} | domain_loss: {:10.6f} | bdc_loss: {:10.6f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=200,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:   0.000000 | total_loss:   4.718077 | domain_loss:  38.109207 | bdc_loss:   1.007952\n",
      "1/1 [==============================] - 0s 89ms/step\n",
      "epoch: 200.000000 | total_loss:   0.583364 | domain_loss:   0.212310 | bdc_loss:   0.624592\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 400.000000 | total_loss:   0.320889 | domain_loss:   0.200456 | bdc_loss:   0.334270\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 600.000000 | total_loss:   0.154046 | domain_loss:   0.188598 | bdc_loss:   0.150207\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 800.000000 | total_loss:   0.072673 | domain_loss:   0.153261 | bdc_loss:   0.063719\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1000.000000 | total_loss:   0.036998 | domain_loss:   0.099915 | bdc_loss:   0.030007\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1200.000000 | total_loss:   0.020420 | domain_loss:   0.062186 | bdc_loss:   0.015779\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1400.000000 | total_loss:   0.012092 | domain_loss:   0.038923 | bdc_loss:   0.009110\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1600.000000 | total_loss:   0.007650 | domain_loss:   0.025440 | bdc_loss:   0.005673\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1800.000000 | total_loss:   0.005140 | domain_loss:   0.017548 | bdc_loss:   0.003761\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2000.000000 | total_loss:   0.003626 | domain_loss:   0.012632 | bdc_loss:   0.002626\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2200.000000 | total_loss:   0.002658 | domain_loss:   0.009384 | bdc_loss:   0.001910\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2400.000000 | total_loss:   0.002002 | domain_loss:   0.007113 | bdc_loss:   0.001434\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2600.000000 | total_loss:   0.001543 | domain_loss:   0.005486 | bdc_loss:   0.001105\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2800.000000 | total_loss:   0.001213 | domain_loss:   0.004285 | bdc_loss:   0.000872\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3000.000000 | total_loss:   0.000969 | domain_loss:   0.003382 | bdc_loss:   0.000701\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3200.000000 | total_loss:   0.000786 | domain_loss:   0.002700 | bdc_loss:   0.000573\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3400.000000 | total_loss:   0.000644 | domain_loss:   0.002180 | bdc_loss:   0.000473\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3600.000000 | total_loss:   0.000533 | domain_loss:   0.001774 | bdc_loss:   0.000395\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3800.000000 | total_loss:   0.000443 | domain_loss:   0.001452 | bdc_loss:   0.000331\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4000.000000 | total_loss:   0.000370 | domain_loss:   0.001195 | bdc_loss:   0.000278\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4200.000000 | total_loss:   0.000310 | domain_loss:   0.000987 | bdc_loss:   0.000235\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4400.000000 | total_loss:   0.000260 | domain_loss:   0.000818 | bdc_loss:   0.000198\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 4600.000000 | total_loss:   0.000218 | domain_loss:   0.000680 | bdc_loss:   0.000167\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4800.000000 | total_loss:   0.000183 | domain_loss:   0.000567 | bdc_loss:   0.000141\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 5000.000000 | total_loss:   0.000154 | domain_loss:   0.000474 | bdc_loss:   0.000119\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5200.000000 | total_loss:   0.000130 | domain_loss:   0.000398 | bdc_loss:   0.000100\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5400.000000 | total_loss:   0.000109 | domain_loss:   0.000335 | bdc_loss:   0.000084\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5600.000000 | total_loss:   0.000092 | domain_loss:   0.000284 | bdc_loss:   0.000071\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5800.000000 | total_loss:   0.000078 | domain_loss:   0.000241 | bdc_loss:   0.000060\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6000.000000 | total_loss:   0.000067 | domain_loss:   0.000206 | bdc_loss:   0.000051\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6200.000000 | total_loss:   0.000057 | domain_loss:   0.000177 | bdc_loss:   0.000043\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6400.000000 | total_loss:   0.000049 | domain_loss:   0.000154 | bdc_loss:   0.000037\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6600.000000 | total_loss:   0.000042 | domain_loss:   0.000134 | bdc_loss:   0.000032\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 6800.000000 | total_loss:   0.000036 | domain_loss:   0.000118 | bdc_loss:   0.000027\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7000.000000 | total_loss:   0.000031 | domain_loss:   0.000104 | bdc_loss:   0.000023\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7200.000000 | total_loss:   0.000028 | domain_loss:   0.000093 | bdc_loss:   0.000020\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7400.000000 | total_loss:   0.000024 | domain_loss:   0.000084 | bdc_loss:   0.000018\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7600.000000 | total_loss:   0.000021 | domain_loss:   0.000076 | bdc_loss:   0.000015\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7800.000000 | total_loss:   0.000019 | domain_loss:   0.000069 | bdc_loss:   0.000014\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8000.000000 | total_loss:   0.000017 | domain_loss:   0.000063 | bdc_loss:   0.000012\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8200.000000 | total_loss:   0.000015 | domain_loss:   0.000058 | bdc_loss:   0.000011\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8400.000000 | total_loss:   0.000014 | domain_loss:   0.000053 | bdc_loss:   0.000010\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8600.000000 | total_loss:   0.000013 | domain_loss:   0.000049 | bdc_loss:   0.000009\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8800.000000 | total_loss:   0.000012 | domain_loss:   0.000046 | bdc_loss:   0.000008\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9000.000000 | total_loss:   0.000011 | domain_loss:   0.000042 | bdc_loss:   0.000007\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9200.000000 | total_loss:   0.000010 | domain_loss:   0.000040 | bdc_loss:   0.000006\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9400.000000 | total_loss:   0.000009 | domain_loss:   0.000037 | bdc_loss:   0.000006\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9600.000000 | total_loss:   0.000008 | domain_loss:   0.000035 | bdc_loss:   0.000005\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9800.000000 | total_loss:   0.000008 | domain_loss:   0.000033 | bdc_loss:   0.000005\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10000.000000 | total_loss:   0.000007 | domain_loss:   0.000031 | bdc_loss:   0.000005\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10200.000000 | total_loss:   0.000007 | domain_loss:   0.000029 | bdc_loss:   0.000004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10400.000000 | total_loss:   0.000006 | domain_loss:   0.000028 | bdc_loss:   0.000004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10600.000000 | total_loss:   0.000006 | domain_loss:   0.000026 | bdc_loss:   0.000004\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 10800.000000 | total_loss:   0.000006 | domain_loss:   0.000025 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 11000.000000 | total_loss:   0.000005 | domain_loss:   0.000024 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 11200.000000 | total_loss:   0.000005 | domain_loss:   0.000022 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 11400.000000 | total_loss:   0.000005 | domain_loss:   0.000021 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11600.000000 | total_loss:   0.000004 | domain_loss:   0.000020 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11800.000000 | total_loss:   0.000004 | domain_loss:   0.000020 | bdc_loss:   0.000003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12000.000000 | total_loss:   0.000004 | domain_loss:   0.000019 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12200.000000 | total_loss:   0.000004 | domain_loss:   0.000018 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12400.000000 | total_loss:   0.000004 | domain_loss:   0.000017 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12600.000000 | total_loss:   0.000004 | domain_loss:   0.000017 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 12800.000000 | total_loss:   0.000003 | domain_loss:   0.000016 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 13000.000000 | total_loss:   0.000003 | domain_loss:   0.000016 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13200.000000 | total_loss:   0.000003 | domain_loss:   0.000016 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13400.000000 | total_loss:   0.000003 | domain_loss:   0.000015 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13600.000000 | total_loss:   0.000003 | domain_loss:   0.000015 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13800.000000 | total_loss:   0.000003 | domain_loss:   0.000015 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 14000.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 14200.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14400.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14600.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14800.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000002\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 15000.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15200.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15400.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 15600.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 15800.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 16000.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16200.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 16400.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 16600.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16800.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17000.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17200.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 17400.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17600.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17800.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18000.000000 | total_loss:   0.000003 | domain_loss:   0.000014 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 18200.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 18400.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18600.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18800.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19000.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19200.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19400.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19600.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 19800.000000 | total_loss:   0.000003 | domain_loss:   0.000013 | bdc_loss:   0.000001\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "Training time cost:   41.51507 seconds\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "uxy_list = []\n",
    "vxy_list = []\n",
    "dudx_list = []\n",
    "dvdx_list = []\n",
    "dudy_list = []\n",
    "dvdy_list = []\n",
    "d2udx2_list = []\n",
    "d2vdx2_list = []\n",
    "d2udy2_list = []\n",
    "d2vdy2_list = []\n",
    "d2udxdy_list = []\n",
    "d2vdxdy_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", loss_weights = [0.1, 0.9])\n",
    "\n",
    "# Record the training time cost\n",
    "start = time.time()\n",
    "\n",
    "# Training\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# Record the training time cost\n",
    "end = time.time()\n",
    "print(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file = open(\"time_costs.txt\", \"w\")\n",
    "time_costs_text_file.write(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file.close()\n",
    "\n",
    "# Load the best weights\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. LOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 10ms/step\n"
     ]
    }
   ],
   "source": [
    "[_, _, uxy, vxy, dudx, dvdx, dudy, dvdy, d2udx2, d2vdx2, d2udy2, d2vdy2, d2udxdy, d2vdxdy] = pde_model.predict(training_input_data[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Results overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1200 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,constrained_layout=False, figsize=(14, 12))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "cs = ax.contourf(x_domain, y_domain, uxy[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, U(x_mesh,y_mesh),20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[2]\n",
    "cs = ax.contourf(x_domain, y_domain, vxy[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[3]\n",
    "cs = ax.contourf(x_domain, y_domain, V(x_mesh,y_mesh),20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "X = pde_model.predict(training_input_data[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "V1 = (X[4]+X[11]+X[3]-(X[2]**2+X[3]**2)*X[3])**2\n",
    "V2 = (X[5]-X[10]-X[2]+(X[2]**2+X[3]**2)*X[2])**2\n",
    "V12 = V1+V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0029186802"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V12.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2,constrained_layout=False, figsize=(14, 5))\n",
    "#\n",
    "ax = axs.ravel()[0]\n",
    "cs = ax.contourf(x_domain, y_domain, V1[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[1]\n",
    "cs = ax.contourf(x_domain, y_domain, V12[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[2]\n",
    "cs = ax.contourf(x_domain, y_domain, V2[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')\n",
    "#\n",
    "ax = axs.ravel()[3]\n",
    "cs = ax.contourf(x_domain, y_domain, V12[0,:,:,0],20)\n",
    "fig.colorbar(cs, ax=ax, shrink=0.9)\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylabel('y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 97ms/step\n"
     ]
    }
   ],
   "source": [
    "V_train = pde_model_train.predict(training_input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[[1.1215750e-06],\n",
       "          [3.9537113e-07],\n",
       "          [2.7639860e-06],\n",
       "          [5.7126608e-06],\n",
       "          [4.0969758e-06],\n",
       "          [4.1386274e-06],\n",
       "          [3.6201732e-06],\n",
       "          [6.0353395e-06],\n",
       "          [8.5937154e-06],\n",
       "          [7.9471083e-06]],\n",
       " \n",
       "         [[2.6629963e-03],\n",
       "          [2.7911691e-03],\n",
       "          [2.8043143e-03],\n",
       "          [2.6515217e-03],\n",
       "          [2.5595922e-03],\n",
       "          [2.3757298e-03],\n",
       "          [2.1363869e-03],\n",
       "          [1.9160900e-03],\n",
       "          [1.5875002e-03],\n",
       "          [5.5763223e-05]],\n",
       " \n",
       "         [[4.5327158e-03],\n",
       "          [4.5563434e-03],\n",
       "          [4.2826952e-03],\n",
       "          [4.2302054e-03],\n",
       "          [3.9841481e-03],\n",
       "          [3.6555917e-03],\n",
       "          [3.4084665e-03],\n",
       "          [2.8407478e-03],\n",
       "          [2.4916695e-03],\n",
       "          [8.5590946e-05]],\n",
       " \n",
       "         [[6.3353786e-03],\n",
       "          [5.7272874e-03],\n",
       "          [5.6080781e-03],\n",
       "          [5.2720145e-03],\n",
       "          [4.9168216e-03],\n",
       "          [4.6380530e-03],\n",
       "          [3.9247442e-03],\n",
       "          [3.5539402e-03],\n",
       "          [2.8597913e-03],\n",
       "          [8.0843078e-05]],\n",
       " \n",
       "         [[7.1467613e-03],\n",
       "          [6.6391462e-03],\n",
       "          [6.2756473e-03],\n",
       "          [5.9052869e-03],\n",
       "          [5.5803331e-03],\n",
       "          [4.8254859e-03],\n",
       "          [4.4410853e-03],\n",
       "          [3.6399516e-03],\n",
       "          [3.0183166e-03],\n",
       "          [7.3001735e-05]],\n",
       " \n",
       "         [[7.0925886e-03],\n",
       "          [6.8947347e-03],\n",
       "          [6.4517530e-03],\n",
       "          [6.1297501e-03],\n",
       "          [5.3727408e-03],\n",
       "          [4.9565905e-03],\n",
       "          [4.1117007e-03],\n",
       "          [3.5084493e-03],\n",
       "          [2.8434230e-03],\n",
       "          [9.2428818e-05]],\n",
       " \n",
       "         [[6.1816610e-03],\n",
       "          [6.2828641e-03],\n",
       "          [6.1594937e-03],\n",
       "          [5.4696277e-03],\n",
       "          [5.0503700e-03],\n",
       "          [4.2212666e-03],\n",
       "          [3.6911452e-03],\n",
       "          [3.0376310e-03],\n",
       "          [2.4347040e-03],\n",
       "          [1.7039553e-04]],\n",
       " \n",
       "         [[4.5453478e-03],\n",
       "          [5.3168726e-03],\n",
       "          [4.9633430e-03],\n",
       "          [4.5666029e-03],\n",
       "          [3.8644874e-03],\n",
       "          [3.4212950e-03],\n",
       "          [2.8194217e-03],\n",
       "          [2.3250764e-03],\n",
       "          [1.8995894e-03],\n",
       "          [1.7480532e-04]],\n",
       " \n",
       "         [[2.9113968e-03],\n",
       "          [3.3804222e-03],\n",
       "          [3.2697292e-03],\n",
       "          [2.8790813e-03],\n",
       "          [2.5464238e-03],\n",
       "          [2.0974842e-03],\n",
       "          [1.7797361e-03],\n",
       "          [1.4730375e-03],\n",
       "          [1.2098418e-03],\n",
       "          [1.0431271e-04]],\n",
       " \n",
       "         [[3.4762252e-07],\n",
       "          [5.9813419e-06],\n",
       "          [6.0003146e-07],\n",
       "          [4.9966502e-06],\n",
       "          [3.4558147e-06],\n",
       "          [3.6382908e-06],\n",
       "          [5.3438785e-06],\n",
       "          [6.7566370e-06],\n",
       "          [1.1108316e-05],\n",
       "          [8.5493912e-06]]]], dtype=float32),\n",
       " array([[9.7842538e-04, 1.6412408e-03, 1.6151496e-03, 1.5634592e-03,\n",
       "         1.4919367e-03, 1.3945943e-03, 1.2667784e-03, 1.1194109e-03,\n",
       "         9.5525139e-04, 2.3907663e-05, 9.7842538e-04, 7.4704224e-04,\n",
       "         1.3771662e-03, 2.0107261e-03, 2.2845888e-03, 2.0062218e-03,\n",
       "         1.2428940e-03, 4.2377095e-04, 1.9000552e-04, 1.0693392e-03,\n",
       "         1.0693392e-03, 2.0255300e-03, 1.9324851e-03, 1.7114775e-03,\n",
       "         1.4752697e-03, 1.2525643e-03, 1.0495095e-03, 8.6787774e-04,\n",
       "         7.1528240e-04, 4.1187574e-05, 2.3907663e-05, 4.3544293e-04,\n",
       "         6.8922737e-04, 7.8175910e-04, 8.1463729e-04, 8.0805423e-04,\n",
       "         6.1641389e-04, 5.8838533e-04, 3.3237509e-04, 4.1187574e-05]],\n",
       "       dtype=float32)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'y_domain':y_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'uxy_list':np.concatenate(uxy_list,axis=0),\n",
    "                     'vxy_list':np.concatenate(vxy_list,axis=0),\n",
    "                     'dudx_list':np.concatenate(dudx_list,axis=0),\n",
    "                     'dvdx_list':np.concatenate(dvdx_list,axis=0),\n",
    "                     'dudy_list':np.concatenate(dudy_list,axis=0),\n",
    "                     'dvdy_list':np.concatenate(dvdy_list,axis=0),\n",
    "                     'd2udx2_list':np.concatenate(d2udx2_list,axis=0),\n",
    "                     'd2vdx2_list':np.concatenate(d2vdx2_list,axis=0),\n",
    "                     'd2udy2_list':np.concatenate(d2udy2_list,axis=0),\n",
    "                     'd2vdy2_list':np.concatenate(d2vdy2_list,axis=0),\n",
    "                     'd2udxdy_list':np.concatenate(d2udxdy_list,axis=0),\n",
    "                     'd2vdxdy_list':np.concatenate(d2vdxdy_list,axis=0)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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